Senior Azure Data Engineer

Korn Ferry
Atlanta, GA

Korn Ferry has partnered with our client on their search for Senior Azure Data Engineer


Position: Senior Azure Data Engineer

Company: Confidential

Location: Atlanta, GA (hybrid remote/onsite)


PURPOSE OF THE POSITION

The Senior Azure Data Engineer role is focused on building and maintaining the scalable data foundation that powers enterprise reporting, analytics, and business decision-making across the organization.


At a high level, this person will serve as the bridge between raw operational data and trusted business insights—ensuring data from multiple systems is centralized, standardized, validated, and delivered in a way that supports reliable reporting and long-term scalability.


This is not simply a pipeline-development role — it is a strategic data engineering position focused on creating a governed, analytics-ready data environment that enables consistency, transparency, and trust across the business. The role will play a key part in improving data quality, reducing reporting issues, and supporting a modern enterprise data platform built around Microsoft Fabric and Azure technologies.


RESPONSIBILITIES

Data Engineering & Pipelines

  • Design, build, and maintain scalable, reliable data pipelines using Microsoft Fabric (Data Factory, Lakehouse, Warehouses, Notebooks).
  • Ingest data from diverse sources (databases, APIs, files, SaaS platforms) into centralized data platforms.
  • Implement efficient batch and incremental data processing patterns.
  • Monitor, troubleshoot, and optimize pipeline performance, reliability, and cost.

Data Transformation & Standardization

  • Cleanse, transform, and standardize data across multiple systems to establish consistent definitions and metrics.
  • Engineer curated, analytics-ready datasets for reporting, dashboards, and downstream analytics tools.
  • Apply data modeling best practices to support enterprise reporting and self-service analytics.

Data Quality & Validation

  • Implement data validation, reconciliation, and quality checks to ensure accuracy, completeness, and reliability.
  • Partner with analytics and business teams to define and enforce data quality rules.
  • Proactively identify and remediate data issues before they impact reporting.

Data Governance & Metadata

  • Manage and mitigate schema drift across pipelines and datasets.
  • Build and maintain data catalogs, including business-friendly metadata, descriptions, and ownership.
  • Enable and maintain data lineage to provide transparency into data sources, transformations, and downstream consumption.
  • Support governance initiatives related to data standards, auditability, and compliance.

Collaboration & Enablement

  • Collaborate with IT, BI developers, analysts, and stakeholders to align system knowledge and data requirements with scalable, enterprise-grade solutions.
  • Contribute to data engineering best practices, standards, and documentation.


REQUIRED QUALIFICATIONS

  • 5+ years of experience as a Data Engineer or in a similar role.
  • Strong experience with Azure data services, with hands-on expertise in Microsoft Fabric.
  • Familiarity with medallion (bronze/silver/gold) data architecture patterns.
  • Proven experience designing and maintaining end-to-end data pipelines.
  • Solid SQL and Python skills and experience with data transformation and modeling.
  • Experience handling schema evolution and schema drift.
  • Hands-on experience with data quality validation and reconciliation processes.
  • Demonstrated experience in data governance concepts such as cataloging, metadata, and lineage.
  • Strong problem-solving skills and attention to detail.
  • Experience with CI/CD, version control, and infrastructure-as-code in data platforms.
  • Knowledge of data security, access controls, and compliance requirements.
  • Experience working in an Agile or DevOps environment.


What Success Looks Like

  • Reliable, well-documented pipelines delivering trusted, standardized data.
  • Clear visibility into data lineage, ownership, and quality.
  • Reduced data issues impacting reporting and analytics.
  • A scalable, governed data platform that supports current and future business needs.
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